Aeromedical Battlefield Evacuation Under Endogenous Uncertainty in Casualty Delivery Times

Abstract

We propose a new medical evacuation (MEDEVAC) model with endogenous uncertainty in the casualty delivery times. The goal is to provide timely medical treatment to injured soldiers and a prompt evacuation via air ambulances. The model determines where to locate medical treatment facilities (MTF) and air ambulances, how to dispatch air ambulances to the point-of-injury, and to which MTF to channel the casualty. The model captures the effect of a delayed MEDEVAC response on the survivability of soldiers, enforces the Golden Hour evacuation doctrine, and represents the availability of air ambulances as an endogenous source of uncertainty since it is contingent on the locations of MTFs. The MEDEVAC model is an MINLP problem whose continuous relaxation is in general nonconvex and for which we develop a new algorithmic method articulated around two main components: i) new bounding techniques obtained through the solution of restriction and relaxation problems, and ii) a spatial branch-and-bound algorithm solving conic mixed-integer programs at each node of the tree. The computational study based on data from the Operation Enduring Freedom reveals that: the bounding problems can be quickly solved regardless of the problem size; the bounds are tight; and the spatial branch-and-bound dominates the Cplex and the Baron solvers in terms of both computational time and robustness. As compared to the standard MEDEVAC myopic policy, our approach increases the number of casualties treated timely and contributes to reducing the number of deaths on the battlefield. The benefits increase as the MEDEVAC resources become tighter and the combats intensify. The model can be used at the strategic level to design an efficient MEDEVAC system and at the tactical level for intelligent tasking and dispatching. Additionally, this study provides valuable contributions to the civilian emergency care community and to the MINLP discipline.